class_docstr: A preprocessing layer which randomly crops images during training.
  
  During training, this layer will randomly choose a location to crop images
  down to a target size. The layer will crop all the images in the same batch
  to the same cropping location.
  
  At inference time, and during training if an input image is smaller than the
  target size, the input will be resized and cropped so as to return the
  largest possible window in the image that matches the target aspect ratio.
  If you need to apply random cropping at inference time, set `training` to
  True when calling the layer.
  
  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
  of integer or floating point dtype. By default, the layer will output
  floats.
  
  **Note:** This layer is safe to use inside a `tf.data` pipeline
  (independently of which backend you're using).
  
  Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.
  
  Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., target_height, target_width, channels)`.
  
  Args:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
      seed: Integer. Used to create a random seed.
      **kwargs: Base layer keyword arguments, such as
          `name` and `dtype`.
code: def get_config(self):
      config = super().get_config()
      config.update(
          {
              "height": self.height,
              "width": self.width,
              "seed": self.seed,
              "data_format": self.data_format,
          }
      )
      return config
  
file_path: keras/keras/layers/preprocessing/random_crop.py
imports: from keras import backend
  from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
  from keras.random.seed_generator import SeedGenerator
  from keras.utils import image_utils
label_desc: Use image node_id 4 for calling the RandomCrop obj's underlying member method code with example usage: obj.get_config() and returns: config